pyanno4rt.optimization.components._squared_overdosing

Squared overdosing component.

Overview

Classes

SquaredOverdosing

Squared overdosing component class.

Function

compute(dose, parameter_value)	Compute the component value.
differentiate(dose, parameter_value, number_of_voxels, segment_indices)	Compute the component gradient.

Classes

class pyanno4rt.optimization.components._squared_overdosing.SquaredOverdosing(maximum_dose=None, embedding='active', weight=1.0, rank=1, bounds=None, link=None, identifier=None, display=True)

Bases: pyanno4rt.optimization.components.ConventionalComponentClass

Squared overdosing component class.

This class provides methods to compute the value and the gradient of the squared overdosing component.

Parameters:

• maximum_dose (int or float) – Maximum value for the dose.

• embedding ({'active', 'passive'}, default='active') – Mode of embedding for the component. In ‘passive’ mode, the component value is computed and tracked, but not considered in the optimization problem, unlike in ‘active’ mode.

• weight (int or float, default=1.0) – Weight of the component function.

• rank (int, default=1) – Rank of the component in the lexicographic order.

• bounds (None or list, default=None) – Constraint bounds for the component.

• link (None or list, default=None) – Other segments used for joint evaluation.

• identifier (None or str, default=None) – Additional string for naming the component.

• display (bool, default=True) – Indicator for the display of the component.

parameter_value

Value of the component parameters.

Type:

list

Overview

Methods

compute_value(*args)	Return the component value from the jitted ‘compute’ function.
compute_gradient(*args)	Return the component gradient from the jitted ‘differentiate’ function.

Members

compute_value(*args)

Return the component value from the jitted ‘compute’ function.

Parameters:

*args (tuple) – Keyworded parameters, where args[0] must be the dose vector(s) to evaluate.

Returns:

Value of the component function.

Return type:

float

compute_gradient(*args)

Return the component gradient from the jitted ‘differentiate’ function.

Parameters:

*args (tuple) – Keyworded parameters, where args[0] must be the dose vector(s) to evaluate and args[1] the corresponding segment(s).

Returns:

Value of the component gradient.

Return type:

ndarray

Functions

pyanno4rt.optimization.components._squared_overdosing.compute(dose, parameter_value)

Compute the component value.

Parameters:

• dose (tuple) – Values of the dose in the segment(s).

• parameter_value (list) – Value of the component parameters.

Returns:

Value of the component function.

Return type:

float

pyanno4rt.optimization.components._squared_overdosing.differentiate(dose, parameter_value, number_of_voxels, segment_indices)

Compute the component gradient.

Parameters:

• dose (tuple) – Values of the dose in the segment(s).

• parameter_value (list) – Value of the component parameters.

• number_of_voxels (int) – Total number of dose voxels.

• segment_indices (tuple) – Indices of the segment(s).

Returns:

Value of the component gradient.

Return type:

ndarray